Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis

Abstract

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed. Furthermore, it was shown that the ratio between α-helix and β-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra. The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

Keywords: Dried human serum; Fourier Transform InfraRed (FTIR); Grid search (GS); Machine learning; Primary myelofibrosis; Principal components analysis (PCA); Support Vector Machine (SVM).